Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Pharmaceutical Design
  4. Volume 30, Issue 36
  5. Article
Volume 30, Issue 36
  • ISSN: 1381-6128
  • E-ISSN: 1873-4286

Abstract

Introduction

serovar Enteritidis and serovar Typhimurium are among the main causative agents of nontyphoidal infections, imposing a significant global health burden. The emergence of antibiotic resistance in these pathogens underscores the need for innovative therapeutic strategies.

Objective

To identify proteins as potential drug targets against and serovars using approaches.

Methods

In this study, a subtractive genomics approach was employed to identify potential drug targets. The whole proteome of PT4 and (D23580), containing 393 and 478 proteins, respectively, was analyzed through subtractive genomics to identify human homologous proteins of the pathogen and also the proteins linked to shared metabolic pathways of pathogen and its host.

Results

Subsequent analysis revealed 19 common essential proteins shared by both strains. To ensure host-specificity, we identified 10 non-homologous proteins absent in humans. Among these proteins, peptidoglycan glycosyltransferase FtsI was pivotal, participating in pathogen-specific pathways and making it a promising drug target. Molecular docking highlighted two potential compounds, Balsamenonon A and 3,3',4',7-Tetrahydroxyflavylium, with strong binding affinities with FtsI. A 100 ns molecular dynamics simulation having 10,000 frames substantiated the strong binding affinity and demonstrated the enduring stability of the predicted compounds at the docked site.

Conclusion

The findings in this study provide the foundation for drug development strategies against infections, which can contribute to the prospective development of natural and cost-effective drugs targeting and .

© 2024 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cpd/10.2174/0113816128332400240827061932
2024-10-01
2024-10-09

  • From This Site

    /content/journals/cpd/10.2174/0113816128332400240827061932
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. FookesM. SchroederG.N. LangridgeG.C. BlondelC.J. MamminaC. ConnorT.R. Seth-SmithH. VernikosG.S. RobinsonK.S. SandersM. PettyN.K. KingsleyR.A. BäumlerA.J. NuccioS.P. ContrerasI. SantiviagoC.A. MaskellD. BarrowP. HumphreyT. NastasiA. RobertsM. FrankelG. ParkhillJ. DouganG. ThomsonN.R. Salmonella bongori provides insights into the evolution of the Salmonellae.PLoS Pathog.201178e100219110.1371/journal.ppat.100219121876672
     [Google Scholar]
  2. OludairoO.O. KwagaJ.K. KabirJ. AbduP.A. GitanjaliA. PerretsA. CibinV. LettiniA. AiyedunJ. A review on Salmonella characteristics, taxonomy, nomenclature with special reference to non-typhoidal and typhoidal salmonellosis.Zagazig Vet. J.202250161176
     [Google Scholar]
  3. ParkE. The genomic epidemiology of typhoidal and invasive nontyphoidal Salmonella in sub-Saharan Africa.University of Oxford2019
     [Google Scholar]
  4. Saleh Mohammed JajereSMJ A review of Salmonella enterica with particular focus on the pathogenicity and virulence factors, host specificity and antimicrobial resistance including multidrug resistance.Vet World2019124504521
     [Google Scholar]
  5. WilsonM. WilsonP.J. WilsonM. Gastroenteritis due to Salmonella.Close Encounters of the Microbial KindResearchgate2021451461
     [Google Scholar]
  6. RaspoetR. Survival strategies of Salmonella enteritidis to cope with antibacterial factors in the chicken oviduct and in egg white. Ghent University 2014.
  7. MuthumbiE. Understanding the carriage and transmission of non- typhoidal Salmonella infections in Kenya.London School of Hygiene & Tropical Medicine2024
     [Google Scholar]
  8. CanalsR. ChaudhuriR.R. SteinerR.E. OwenS.V. Quinones-OlveraN. GordonM.A. BaymM. IbbaM. HintonJ.C.D. The fitness landscape of the African Salmonella typhimurium ST313 strain D23580 reveals unique properties of the pBT1 plasmid.PLoS Pathog.2019159e100794810.1371/journal.ppat.100794831560731
     [Google Scholar]
  9. WangX. BiswasS. PaudyalN. PanH. LiX. FangW. YueM. Antibiotic resistance in Salmonella typhimurium isolates recovered from the food chain through national antimicrobial resistance monitoring system between 1996 and 2016.Front. Microbiol.20191098510.3389/fmicb.2019.0098531134024
     [Google Scholar]
  10. HurJ. KimJ.H. ParkJ.H. LeeY.J. LeeJ.H. Molecular and virulence characteristics of multi-drug resistant Salmonella enteritidis strains isolated from poultry.Vet. J.2011189330631110.1016/j.tvjl.2010.07.01720822940
     [Google Scholar]
  11. KumariH. KumarK. KumarG. Acute gastroenteritis: Its causes, maintenance, and treatment.J. Pharm. Negat. Results.202213850645078
     [Google Scholar]
  12. ShahidF. ShehrozM. ZaheerT. AliA.J.F.A-I.D.D. Subtractive genomics approaches: Towards anti-bacterial drug discovery.Front. Anti-Infect. Drug Discov.2020815144158
     [Google Scholar]
  13. NaoremR.S. PangabamB.D. BoraS.S. GoswamiG. BarooahM. HazarikaD.J. FeketeC. Identification of putative vaccine and drug targets against the methicillin-resistant Staphylococcus aureus by reverse vaccinology and subtractive genomics approaches.Molecules2022277208310.3390/molecules2707208335408485
     [Google Scholar]
  14. KhanK. AlharM.S.O. AbbasM.N. AbbasS.Q. KaziM. KhanS.A. SadiqA. HassanS.S. BungauS. JalalK. Integrated bioinformatics-based subtractive genomics approach to decipher the therapeutic drug target and its possible intervention against brucellosis.Bioengineering202291163310.3390/bioengineering911063336354544
     [Google Scholar]
  15. KumarA. ThotakuraP.L. TiwaryB.K. Target identification in Fusobacterium nucleatum by subtractive genomics approach and enrichment analysis of host-pathogen protein-protein interactions.201616112
     [Google Scholar]
  16. RathiB. SarangiA.N. TrivediN. Genome subtraction for novel target definition in Salmonella typhi.Bioinformation20094414315010.6026/9732063000414320198190
     [Google Scholar]
  17. UniProt Consortium UniProt: A hub for protein information.Nucleic Acids Res.201543Database issueD204D21225348405
     [Google Scholar]
  18. WenQ.F. LiuS. DongC. GuoH.X. GaoY.Z. GuoF.B. Geptop 2.0: An updated, more precise, and faster Geptop server for identification of prokaryotic essential genes.Front. Microbiol.201910123610.3389/fmicb.2019.0123631214154
     [Google Scholar]
  19. ZhangC. ZhengW. FreddolinoP.L. ZhangY. MetaGO: Predicting gene ontology of non-homologous proteins through low-resolution protein structure prediction and protein–protein network mapping.J. Mol. Biol.2018430152256226510.1016/j.jmb.2018.03.00429534977
     [Google Scholar]
  20. DuJ. YuanZ. MaZ. SongJ. XieX. ChenY. KEGG-PATH: Kyoto encyclopedia of genes and genomes-based pathway analysis using a path analysis model.Mol. Biosyst.20141092441244710.1039/C4MB00287C24994036
     [Google Scholar]
  21. GardyJ.L. LairdM.R. ChenF. ReyS. WalshC.J. EsterM. BrinkmanF.S.L. PSORTb v.2.0: Expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis.Bioinformatics200521561762310.1093/bioinformatics/bti05715501914
     [Google Scholar]
  22. YuN.Y. WagnerJ.R. LairdM.R. MelliG. ReyS. LoR. DaoP. SahinalpS.C. EsterM. FosterL.J. BrinkmanF.S.L. PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.Bioinformatics201026131608161510.1093/bioinformatics/btq24920472543
     [Google Scholar]
  23. BhasinM. GargA. RaghavaG.P.S. PSLpred: Prediction of subcellular localization of bacterial proteins.Bioinformatics200521102522252410.1093/bioinformatics/bti30915699023
     [Google Scholar]
  24. YinR. FengB.Y. VarshneyA. PierceB.G. Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants.Protein Sci.2022318e437910.1002/pro.437935900023
     [Google Scholar]
  25. SelvamK. SenbagamD. SelvankumarT. SudhakarC. Kamala-KannanS. SenthilkumarB. Cellulase enzyme: Homology modeling, binding site identification and molecular docking.J. Mol. Struct.201711506167
     [Google Scholar]
  26. WiedersteinM. SipplM.J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins.Nucleic Acids Res.200735Web ServerW407W41010.1093/nar/gkm29017517781
     [Google Scholar]
  27. WallnerB. ElofssonA. Prediction of global and local model quality in CASP7 using Pcons and ProQ.Proteins200769S8Suppl. 818419310.1002/prot.2177417894353
     [Google Scholar]
  28. LiY.Y. AnJ. JonesS.J.M. A computational approach to finding novel targets for existing drugs.PLOS Comput. Biol.201179e100213910.1371/journal.pcbi.100213921909252
     [Google Scholar]
  29. FerreiraL. Dos SantosR. OlivaG. AndricopuloA. Molecular docking and structure-based drug design strategies.Molecules2015207133841342110.3390/molecules20071338426205061
     [Google Scholar]
  30. DallakyanS. Small-molecule library screening by docking with PyRx.Methods Mol Biol20151263243250
     [Google Scholar]
  31. StylianakisI. ZervosN. LiiJ.H. PantazisD.A. KolocourisA. Conformational energies of reference organic molecules: benchmarking of common efficient computational methods against coupled cluster theory.J. Comput. Aided Mol. Des.2023371260765610.1007/s10822‑023‑00513‑537597063
     [Google Scholar]
  32. DundasJ. OuyangZ. TsengJ. BinkowskiA. TurpazY. LiangJ. CASTp: Computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues.Nucleic Acids Res.200634Web Server issueW116-816844972
     [Google Scholar]
  33. StudioD.J.A. Discovery studioStudio DJA2008420
     [Google Scholar]
  34. PettersenE.F. GoddardT.D. HuangC.C. MengE.C. CouchG.S. CrollT.I. MorrisJ.H. FerrinT.E. UCSF ChimeraX: Structure visualization for researchers, educators, and developers.Protein Sci.2021301708210.1002/pro.394332881101
     [Google Scholar]
  35. BergdorfM KimET RendlemanCA Desmond/GPU Performance as of November 2014D.E. Shaw Research2014
     [Google Scholar]
  36. AlbaughA. BoatengH.A. BradshawR.T. DemerdashO.N. DziedzicJ. MaoY. MargulD.T. SwailsJ. ZengQ. CaseD.A. EastmanP. WangL.P. EssexJ.W. Head-GordonM. PandeV.S. PonderJ.W. ShaoY. SkylarisC.K. TodorovI.T. TuckermanM.E. Head-GordonT. Advanced potential energy surfaces for molecular simulation.J. Phys. Chem. B2016120379811983227513316
     [Google Scholar]
  37. BolhuisP.G. SwensonD.W.J.A.T. Transition path sampling as Markov chain Monte Carlo of trajectories: Recent algorithms, software, applications, and future outlook.Adv. Theory Simul.2021442000237
     [Google Scholar]
  38. da FonsecaA.M. CaluacoB.J. MadureiraJ.M.C. CabongoS.Q. GaietaE.M. DjataF. ColaresR.P. NetoM.M. FernandesC.F.C. MarinhoG.S.J.M.B. Screening of potential inhibitors targeting the main protease structure of SARS- CoV-2 via molecular docking, and approach with molecular dynamics, RMSD, RMSF, H-bond, SASA and MMGBSA.202311537490200
     [Google Scholar]
  39. BojkovaD. KlannK. KochB. WideraM. KrauseD. CiesekS. CinatlJ. MünchC. Proteomics of SARS-CoV-2- infected host cells reveals therapy targets.Nature2020583781646947210.1038/s41586‑020‑2332‑732408336
     [Google Scholar]
  40. LuisaB.G. Cellular energy metabolism and its regulation.Elsevier2012
     [Google Scholar]
  41. WeissD.S. ChenJ.C. GhigoJ.M. BoydD. BeckwithJ. Localization of FtsI (PBP3) to the septal ring requires its membrane anchor, the Z ring, FtsA, FtsQ, and FtsL.J. Bacteriol.1999181250852010.1128/JB.181.2.508‑520.19999882665
     [Google Scholar]
  42. Di GuilmiA. DessenA. DidebergO. VernetT. Bifunctional penicillin-binding proteins: Focus on the glycosyltransferase domain and its specific inhibitor moenomycin.Curr. Pharm. Biotechnol.200232637510.2174/138920102337843612022260
     [Google Scholar]
  43. CulpE. WrightG.D. Bacterial proteases, untapped antimicrobial drug targets.J. Antibiot.201770436637710.1038/ja.2016.13827899793
     [Google Scholar]
  44. AgarwalS. An overview of molecular docking.JSM Chem2016410241028
     [Google Scholar]
  45. FoloppeN. HubbardR. Towards predictive ligand design with free-energy based computational methods?Curr. Med. Chem.200613293583360810.2174/09298670677902616517168725
     [Google Scholar]
  46. EngS-K. PusparajahP. Ab MutalibN-S. SerH-L. ChanK-G. Salmonella: A review on pathogenesis, epidemiology and antibiotic resistance.Front. Life Sci.201583284293
     [Google Scholar]
  47. KambojS. GuptaN. BandralJ.D. GandotraG. Food safety and hygiene: A review.Int. J. Chem. Stud.20208358368
     [Google Scholar]
  48. SolanaJ. Garrote-SánchezE. DELEAT: Gene essentiality prediction and deletion design for bacterial genome reduction.202122117
     [Google Scholar]
  49. MuzziA. MasignaniV. RappuoliR. The pan-genome: Towards a knowledge-based discovery of novel targets for vaccines and antibacterials.Drug Discov. Today20071211-1242943910.1016/j.drudis.2007.04.00817532526
     [Google Scholar]
  50. AalberseR.C. AkkerdaasJ. Van ReeR. Cross-reactivity of IgE antibodies to allergens.Allergy200156647849010.1034/j.1398‑9995.2001.056006478.x11421891
     [Google Scholar]
  51. AmejiP.J. UzairuA. ShallangwaG.A. UbaS. Molecular docking-based virtual screening, drug-likeness, and pharmacokinetic profiling of some anti-Salmonella typhimurium cephalosporin derivatives.J. Taibah Univ. Med. Sci.20231861417143110.1016/j.jtumed.2023.05.02138162870
     [Google Scholar]
  52. PutraM.Y. In silico studies of drug discovery and design against COVID-19 focusing on ACE2 and spike protein virus receptors: A systematic review.Sci. Pharm.202323171183
     [Google Scholar]
/content/journals/cpd/10.2174/0113816128332400240827061932
Loading
Identification of Peptidoglycan Glycosyltransferase FtsI as a Potential Drug Target against Salmonella enteritidis and Salmonella typhimurium Serovars through Subtractive Genomics, Molecular Docking and Molecular Dynamics Simulation Approaches
Curr. Pharm. Des. 30, 2882 (2024); https://doi.org/10.2174/0113816128332400240827061932
/content/journals/cpd/10.2174/0113816128332400240827061932
/content/journals/cpd/10.2174/0113816128332400240827061932
Loading

Data & Media loading...

Supplements

file format download
  • Article Type: Research Article
Keyword(s): drug targets; essential proteins, molecular docking; Salmonella enteritidis; Salmonella typhimurium; subtractive genomics

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test